import argparse
import logging
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from networks.vision_transformer import SwinUnet as ViT_seg
from trainer import trainer_synapse, trainer_acdc, trainer_busi, trainer_lung
from config import get_config

parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str,
                    default='./data/BUSI/train_npz', help='root dir for data')
parser.add_argument('--dataset', type=str,
                    default='BUSI', help='experiment_name')
parser.add_argument('--list_dir', type=str,
                    default='./lists/lists_BUSI', help='list dir')
parser.add_argument('--num_classes', type=int,
                    default=2, help='output channel of network')
parser.add_argument('--output_dir', type=str, default='./resnet50_BUSI_model', help='output dir')
parser.add_argument('--max_iterations', type=int,
                    default=30000, help='maximum epoch number to train')
parser.add_argument('--max_epochs', type=int,
                    default=150, help='maximum epoch number to train')
parser.add_argument('--batch_size', type=int,
                    default=32, help='batch_size per gpu')
parser.add_argument('--n_gpu', type=int, default=1, help='total gpu')
parser.add_argument('--deterministic', type=int, default=1,
                    help='whether use deterministic training')
parser.add_argument('--base_lr', type=float, default=0.035,
                    help='segmentation network learning rate')
parser.add_argument('--img_size', type=int,
                    default=224, help='input patch size of network input')
parser.add_argument('--seed', type=int,
                    default=1234, help='random seed')
parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file',
                    default='./configs/swin_tiny_patch4_window7_224_lite.yaml')
parser.add_argument(
    "--opts",
    help="Modify config options by adding 'KEY VALUE' pairs. ",
    default=None,
    nargs='+',
)
parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'],
                    help='no: no cache, '
                         'full: cache all data, '
                         'part: sharding the dataset into nonoverlapping pieces and only cache one piece')
parser.add_argument('--resume', help='resume from checkpoint')
parser.add_argument('--accumulation-steps', type=int, help="gradient accumulation steps")
parser.add_argument('--use-checkpoint', action='store_true',
                    help="whether to use gradient checkpointing to save memory")
parser.add_argument('--amp-opt-level', type=str, default='O1', choices=['O0', 'O1', 'O2'],
                    help='mixed precision opt level, if O0, no amp is used')
parser.add_argument('--tag', help='tag of experiment')
parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
parser.add_argument('--throughput', action='store_true', help='Test throughput only')
args = parser.parse_args()
if args.dataset == "Synapse":
    args.root_path = os.path.join(args.root_path, "train_npz")
config = get_config(args)

if __name__ == "__main__":
    if not args.deterministic:
        cudnn.benchmark = True
        cudnn.deterministic = False
    else:
        cudnn.benchmark = False
        cudnn.deterministic = True

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    dataset_name = args.dataset
    dataset_config = {
        'Synapse': {
            'root_path': args.root_path,
            'list_dir': './lists/lists_Synapse',
            'num_classes': 9,
        },
        'BUSI': {
            'root_path': args.root_path,
            'list_dir': './lists/lists_BUSI',
            'num_classes': 2,
        },
        'Skin': {
            'root_path': args.root_path,
            'list_dir': './lists/lists_Skin',
            'num_classes': 2,
        },
        'lung': {
            'root_path': args.root_path,
            'list_dir': './lists/lists_lung',
            'num_classes': 2,
        },
    }

    if args.batch_size != 24 and args.batch_size % 6 == 0:
        args.base_lr *= args.batch_size / 24
    args.num_classes = dataset_config[dataset_name]['num_classes']
    args.root_path = dataset_config[dataset_name]['root_path']
    args.list_dir = dataset_config[dataset_name]['list_dir']

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    net = ViT_seg(config, img_size=args.img_size, num_classes=args.num_classes)  # 构建Swin-Unet
    net.load_from(config)  # 加载预训练的模型参数，以及相关配置
    #net = net.cuda() if torch.cuda.is_available() else net
    inp = torch.rand(1, 3, 224, 224)
    #inp = inp.cuda() if torch.cuda.is_available() else inp
    from thop import profile

    macs, params = profile(model=net, inputs=(inp,))
    macs = macs / (10 ** 6)
    params = params / (10 ** 6)
    print("SR-Unet:params:{}M Macs:{}M".format(params, macs))
    """
    trainer = {'Synapse': trainer_synapse, 'ACDC': trainer_acdc, "BUSI": trainer_busi, "lung": trainer_lung}
    trainer[dataset_name](args, net, args.output_dir)  # 对模型进行训练，以及模型的保存
"""
